This paper reconsiders the effectiveness of few-shot image classification benchmarks and the role of meta-learning algorithms. The authors propose a simple baseline approach that involves learning a supervised or self-supervised representation on the meta-training set and then training a linear classifier on top of this representation. Surprisingly, this baseline outperforms state-of-the-art few-shot learning methods, often by large margins. The authors attribute this success to the quality of the learned embedding model rather than sophisticated meta-learning algorithms. They further demonstrate that self-distillation on this baseline can provide additional performance boosts. The findings suggest that using a good learned embedding model can be more effective than complex meta-learning algorithms. The paper also shows that representations learned with state-of-the-art self-supervised methods perform similarly to fully supervised methods, indicating that "learning to learn" can be achieved through learning a good self-supervised embedding. The authors provide experimental results on several benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100, and show that their method achieves significant improvements over previous state-of-the-art methods.This paper reconsiders the effectiveness of few-shot image classification benchmarks and the role of meta-learning algorithms. The authors propose a simple baseline approach that involves learning a supervised or self-supervised representation on the meta-training set and then training a linear classifier on top of this representation. Surprisingly, this baseline outperforms state-of-the-art few-shot learning methods, often by large margins. The authors attribute this success to the quality of the learned embedding model rather than sophisticated meta-learning algorithms. They further demonstrate that self-distillation on this baseline can provide additional performance boosts. The findings suggest that using a good learned embedding model can be more effective than complex meta-learning algorithms. The paper also shows that representations learned with state-of-the-art self-supervised methods perform similarly to fully supervised methods, indicating that "learning to learn" can be achieved through learning a good self-supervised embedding. The authors provide experimental results on several benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100, and show that their method achieves significant improvements over previous state-of-the-art methods.